Channel estimation in wireless communication systems is usually accomplished by inserting, along with the information, a series of known symbols, whose analysis is used to define the parameters of the filters that remove the distortion of the data. Nevertheless, a part of the available bandwidth has to be destined to these symbols. Until now, no alternative solution has demonstrated to be fully satisfying for commercial use, but one technique that looks promising is superimposed training (ST). This work describes a hybrid software-hardware FPGA implementation of a recent algorithm that belongs to the ST family, known as Datadependent Superimposed Training (DDST), which does not need extra bandwidth for its training sequences (TS) as it adds them arithmetically to the data. DDST also adds a third sequence known as data-dependent sequence, that destroys the interference caused by the data over the TS. As DDST's computational burden is too high for the commercial processors used in mobile systems, a System on a Programmable Chip (SOPC) approach is used in order to solve the problem.
This paper presents a general and hybrid (centralized and distributed) approach for the activation of processing elements (PEs) inside of a processor array using the polytope model. The proposed approach is suitable of being implemented on reconfigurable systems since by changing some mathematical expressions, the proposed control approach is able to provide activation patterns for different algorithms based on the polytope model. We have taken the Cholesky decomposition as example for developing our hybrid control towards a generalization of this scheme.
This paper presents a module that solves the square root by obtaining a number of more significant bits from a look-up table as an approximate root. A set of possible roots are then appended and squared for comparison to the original radicand, finely tuning the calculation. The module stops as soon as it finds an exact root, therefore not all entries take the same number of cycles, reducing the number of iterations required for full resolution. The proposed FPGA module overcomes a Xilinx's logiCORE IP in terms of resources utilization and in several cases latency due to its flexible structure configuration.
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